1. Field
The following description relates to image processing methods and apparatuses.
2. Description of the Related Art
User demand for high-quality images is increasing. Accordingly, image processing technologies have been continuously developed. In addition, a variety of image processing methods for improving the quality of images have been introduced. Such image processing methods focus on how to improve processing speeds while effectively removing noises from images.
An optical coherence tomography (OCT) is a method of capturing an organization structure within a human body, using a micrometer-resolution. This technology has been widely used in ophthalmic clinics, but has the following problems when used for high-density tissues in dermatologic clinics. Due to a relatively high level of optical attenuation and several occurrences of scattering, a relatively large amount of noise may be generated. This noise may reduce a dynamic range and a signal-to-noise ratio (SNR) of an OCT system. In addition, this noise may make it difficult to distinguish details of an OCT image obtained from a high-density tissue. In this situation, there is a need for image processing methods capable of effectively reducing noises from OCT images. Moreover, in order to provide more human tissue information, there is a need for image processing methods capable of improving the quality of OCT images.
Two types of noise may be included in an OCT image. The noise may include an incoherent noise and a speckle noise. In a general pretreatment method, an incoherent noise may be suppressed through system optimization. However, a speckle noise includes tissue information. In order to remove a speckle noise, separate hardware and scanning are used. Thus, an edge of an image may be blurred, or a shooting speed may be decreased. Therefore, in order to eliminate influence of a speckle noise, the use of several posttreatment methods (for example, the use of a large quantity of digital filters) may prevent a decrease in a shooting speed and reduce phenomenon that an edge of an image is blurred during image pretreatment.
Examples of filters used in the posttreatment may include a linear filter, a nonlinear filter, a diffusion filter, and a wavelet filter. The linear filter and the wavelet filter are used on the assumption that a speckle noise model has a multiplicative form. Therefore, influence of an incoherent noise may be usually negligible, and an incoherent noise may be set to zero. However, the nonlinear filter and the diffusion filter suppress noise based on a local image feature.
The four types of the filter may suppress noise, but the existing filters have the following limitations. First, in view of a noise reduction effect and a processing speed in a filter, a noise removal filter having an excellent effect may not be unsuitable for real-time image processing. Second, in a physical model using such a filter, an inherent noise is assumed as zero, and influence of an inherent noise is not considered. Third, when a noise reduction process of such a filter is used, an image may be blurred by a predetermined level, and a detailed expression of an image may be affected.